The Internet and other communication networks have grown in sophistication in terms of their offering of solutions to connect users with relevant content. Despite this, the Internet and other networks still constitute a vast wilderness of information in which it can be extremely difficult for users to identify information that is assured of being useful and/or interesting. Various approaches exist for addressing this problem, including collaborative filtering and various social recommendation tools.
For example, many e-commerce websites employ functionality that will recommend alternate or supplemental products to a user based on the user's browsing among particular items featured on the site. These recommendations in many cases are based on purchaser data accumulated by the website, for example, data which shows that users buying a first product are also likely to want to buy a second, related product. One issue with such an approach is that there is no mechanism for establishing that a given user will have preferences that are similar to other users of the website. Therefore, the recommendations can be very speculative, and these systems are therefore limited in the ability to provide relevant recommendations to users.